fracspy.mtinversion.mtwi.MTW#

class fracspy.mtinversion.mtwi.MTW(x, y, z, recs, vel, src_idx, comp_idx, omega_p, aoi, t, wav, wavc, Ms_scaling=1.0, engine='numpy', multicomp=False, cosine_sourceangles=None, dists=None)[source]#

Moment-Tensor Waveform modelling and inversion

This class acts as an abstract interface for users to perform moment-tensor modelling of waveforms

Parameters:

xnumpy.ndarray: X-axis
ynumpy.ndarray: Y-axis
znumpy.ndarray: Z-axis
recsnumpy.ndarray: Receiver locations of size \(3 \times n_r\)
velnumpy.ndarray: Velocity model of size \(n_x \times n_y \times n_z\)
src_idxnumpy.ndarray: Source location indices (relative to x, y, and z axes)
comp_idxint: Index of component at receiver side
omega_pfloat: Peak frequency of the given wave
aoituple: Area of interest for waveform computation defined as half windows to place either size of the source in center of region (defined by src_idx)
tnumpy.ndarray: Time axis for data
wavnumpy.ndarray: Wavelet.
wavcenterint: Index of wavelet center
Ms_scalingfloat: Scaling to be incorporated in the MTI
enginestr, optional: Engine used for computations (numpy or numba).
multicomp: obj:boolean: Whether running for single or multicomponent data
cosine_sourceanglesnumpy.ndarray: Cosine source angles of size \(3 \times n_r \times n_x \times n_y \times n_z\)
distsnumpy.ndarray: Distances of size \(\times n_r \times n_x \times n_y \times n_z\)

Methods

`__init__`(x, y, z, recs, vel, src_idx, ...[, ...])
`adjoint`(data)	Adjoint modelling
`invert`(data[, kind])
`lsi`(data[, niter, verbose])
`model`(mt)	Modelling
`sparselsi`(data[, niter, l1eps, verbose])